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skills Interest in PhD Thesis Knowledge in inverse problems and forward models Willingness to be mobile (work in international projects, conferences, etc.) Admission Requirements Completed master's
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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options for sports and cultural activities external link . You can also tailor your employment conditions through our Terms of Employment Options Model. In this way, we encourage you to keep investing in
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on projects that model the work of our full-time employees. You will also go through a comprehensive education program and interact with mentors who are at the top of their field, allowing you to build
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on projects that model the work of our full-time employees. You will also go through a comprehensive education program and interact with mentors who are at the top of their field, allowing you to build
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proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is
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/Associate will be part of Newcastle University’s Electrical Power Group working with the Marine Resources and Renewable Energy Group. The aim of the role is to assist in the design modelling and build of a
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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and
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recordings to test new models of brain-body interaction. We will also explore whether rhythmic stimulation can enhance cognitive performance. The project will give you the opportunity to work at the cutting
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
planning and scheduling. In the furthermore, core works are envisioned in (c) initiating novel VLE design using foundational Digital Twin for Construction Safey (DTCS) components (e.g., city models, nD BIM